The long term goal of this project is to develop non-invasive imaging methods to portray the cytotype of metastatic breast cancer in patients with historical estrogen receptor (ER) positive disease in order to guide alternative targeted therapies. Low metastatic uptake of 18fluoro-estradiol (18FES) by positron emission tomography (PET) can delineate patients that will not benefit from further endocrine therapy without obtaining invasive biopsies. Our hypothesis is that a triad imaging metric that includes ER levels by PET in addition to tumor cellularity and vascularity end-points by magnetic resonance imaging (MRI) will improve individual monitoring of patients with late stage ER+ breast cancer. A major challenge in managing metastatic breast cancer lies in phenotypic heterogeneity of a particular lesion, which can be drastically different from the original primary tumor. This exploratory proposal will address this challenge by establishing three quantitative end-points derived from novel non-invasive imaging protocols for: ER levels by 18FES-PET, metastatic cell density by diffusion weighted MRI (DWI), and angiogenesis by dynamic contrast-enhanced (DCE)-MRI. An impediment to developing diagnostic imaging techniques for late stage ER+ breast cancer is a lack of adequate models that reflect both ER heterogeneity and organ trophic dissemination. Accordingly, for these studies we will use ER+ breast cancer patient-derived xenografts (PDX) that retain the intrinsic ER expression levels of the original tumor/metastases (0-90%). Further, we will use an experimental metastasis model in which PDX are disseminated to the major breast trophic organs. The specific aims of this study are 1) to characterize ER status of multifocal metastasis in patient-derived models of ER+ breast cancers by 18FES-PET/CT, and 2) to characterize the metastatic microenvironment by physiologically-based multi-parametric MRI and develop multi-factor lesion imaging scores. The endpoints of this study are determination of a threshold for 18FES uptake in lesions using a range of ER+ tumors (0 to 90% positivity), development of high-resolution multi-parametric MRI to resolve multifocal metastases, and formulation of predictive numerical scores based on 18FES uptake, tumor cell density (DWI), and vascularization (DCE-MRI). We expect to form further hypotheses regarding a relationship between high/low ER expression (SUV from 18FES-PET/CT), cell density (ADC by DWI) and perfusion (Ktrans from DCE-MRI). The subsequent goal will be to test imaged-based predictive scores on therapeutic treatment of luminal breast cancer metastasis models. This could delineate alternative treatment strategies for ER- or ER+/endocrine resistant metastatic lesions. Our expectation is that these studies will lead to improved individual management of late stage ER+ breast cancer - these account for over half of the all deaths each year from metastatic disease.